Uncover mortality patterns and hospital effects in COVID-19 heart failure patients: a novel Multilevel logistic cluster-weighted modeling approach

TL;DR

This paper introduces a novel multilevel logistic cluster-weighted model to predict 45-day mortality in COVID-19 heart failure patients, accounting for hospital effects and patient heterogeneity, with promising application results in Lombardy, Italy.

Contribution

The paper presents a new multilevel logistic cluster-weighted modeling approach that effectively captures hospital-specific effects and patient heterogeneity in mortality prediction.

Findings

Model accurately predicts 45-day mortality in COVID-19 heart failure patients.

Effective in identifying hospital effects and patient subgroups.

Demonstrates utility in healthcare decision-making and policy planning.

Abstract

Evaluating hospitals' performance and its relation to patients' characteristics is of utmost importance to ensure timely, effective, and optimal treatment. Such a matter is particularly relevant in areas and situations where the healthcare system must contend with an unexpected surge in hospitalizations, such as for heart failure patients in the Lombardy region of Italy during the COVID-19 pandemic. Motivated by this issue, the paper introduces a novel Multilevel Logistic Cluster-Weighted Model (ML-CWMd) for predicting 45-day mortality following hospitalization due to COVID-19. The methodology flexibly accommodates dependence patterns among continuous, categorical, and dichotomous variables; effectively accounting for hospital-specific effects in distinct patient subgroups showing different attributes. A tailored Expectation-Maximization algorithm is developed for parameter estimation, and extensive simulation studies are conducted to evaluate its performance against competing models. The novel approach is applied to administrative data from the Lombardy Region, aiming to profile heart failure patients hospitalized for COVID-19 and investigate the hospital-level impact on their overall mortality. A scenario analysis demonstrates the model's efficacy in managing multiple sources of heterogeneity, thereby yielding promising results in aiding healthcare providers and policy-makers in the identification of patient-specific treatment pathways.